Artificial Neural Network Based Fault Diagnosis of a Pulley-Belt Rotating System

Jaber, Alaa Abdulhady; Ali, Khalid Mohsin

doi:10.18517/ijaseit.9.2.7426

Cited by 12 publications

(5 citation statements)

References 12 publications

(8 reference statements)

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“…It was yarn that must be improved in quality because in the weaving process, the warp yarn was subjected to more workloads, such as pulling and friction. When the warp was opened, the thread was pulled, while friction occurs between the thread and the back roller, dropper, gun, comb, binoculars or with the thread itself, so the warp threads must be strong, have good frictional resistance, and have high elongation (Jaber & Ali, 2019;Polach et al, 2015). This was in accordance with interviews with respondents who said that "warp threads must be stronger than weft threads".…”

Section: The Loomsupporting

confidence: 53%

Indigenous science constructs based on Troso woven fabric local wisdom: a study in ethnoscience and ethnoecology

Khusniati,

Heriyanti,

Aryani

et al. 2023

tused

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Troso woven fabric is a kind of weaving fabric produced in a village in Indonesia called Troso. The aim of this study was to identify the detailed process of manufacture of Troso woven fabric and reconstruct it related to several scientific concepts. Research method started by observe several local cultures in this city, and found that students can better understand the science concept and increase their environmental awareness when it is exemplified by circumstances in their surrounding environment. The results of Troso woven fabric reconstruction can be used as a contextual alternative learning resource and increase students' awareness in protecting their environment. This study uses a qualitative research approach through ethnoscience and ethnoecology. The two aspects of the study were carried out through the process of reconstructing indigenous science into scientific science. The research stages include pre-research preparation, primary data collection through observation, in-depth interviews, discussion, and direct observations. The next step was collecting data from the study of cultural documents on the manufacturing of Troso woven fabric as well as verifying, reconstructing, formulating, and conceptualising into science. Various scientific studies relate to the measurement process, compounds and mixtures, heat, and energy transfer associated with ethnoscience. Ethnoecologically, the community does not know yet about the environmental impact that has occurred, resulting in environmental damage even though the community has not directly observed it. By associating science material with the local culture, it is hoped that the understanding of science will become more meaningful and contextual.

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Section: The Loomsupporting

confidence: 53%

Indigenous science constructs based on Troso woven fabric local wisdom: a study in ethnoscience and ethnoecology

Khusniati,

Heriyanti,

Aryani

et al. 2023

tused

View full text Add to dashboard Cite

show abstract

“…This force can be changed by changing the pulling forces via the tension rod to easily pulling the system, as shown in Figure 1. To put more flexibility in the developed test rig, a slot was made under the motor to make it easy to move linearly to increase or decrease the load on the shaft [32]. Keys were used for fixing the pulleys on the motor's shaft and the bearings' (driven) shaft.…”

Section: Test Rig Fabricationmentioning

confidence: 99%

Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart

Mohammed¹,

Abdulhady²

2021

FME Transactions

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Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.

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“…Based on the nature of the features, the stationary feature can also be classified into deterministic and nondeterministic. Deterministic signals have specific frequency components and can be described by a mathematical equation [11]. Timedomain features are going to be used here as the input to the ANN, and they can be extracted from the captured signals using the following relationships [12]: 1) Root mean square (RMS): measures for all levels of a signal.…”

Section: Time-domain Signal Analysis For Feature Extractionmentioning

confidence: 99%

Bearing Fault Diagnosis Using Motor Current Signature Analysis and the Artificial Neural Network

Dhomad¹,

Jaber²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Bearings are critical components in rotating machinery. The need for easy and effective bearings fault diagnosis techniques has led to developing different monitoring approaches. In this research, however, a fault diagnosis system for bearings is developed based on the motor current signature analysis (MCSA) technique. Firstly, a test rig was built, and then different bearing faults were simulated and investigated in the test rig. Three current sensors, type SCT013, were interfaced to Arduino MEGA 2560 microcontroller and utilized together for data acquisition. The time-domain signals analysis technique was utilized to extract some characteristic features that are related to the simulated faults. It was noticed that the simulated bearing faults have led to generating vibrations in the induction motors, which in turn cause a change in its magnetic field. For classification (identification) of the extracted features, the artificial neural network (ANN) was employed. An ANN model was developed using the Matlab ANN toolbox to detect the simulated faults and give an indication about the machine health state. The obtained features from the captured motor current signals were utilized for training the ANN model. The results showed the effectiveness of using MCSA based on the timedomain signal analysis in combination with ANN in diagnosis different bearings faults.

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Artificial Neural Network Based Fault Diagnosis of a Pulley-Belt Rotating System

Cited by 12 publications

References 12 publications

Indigenous science constructs based on Troso woven fabric local wisdom: a study in ethnoscience and ethnoecology

Indigenous science constructs based on Troso woven fabric local wisdom: a study in ethnoscience and ethnoecology

Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart

Bearing Fault Diagnosis Using Motor Current Signature Analysis and the Artificial Neural Network

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